What is Machine Learning?

Machine learning is a subfield of data mining. While data mining has been around for more than 50+ years, machine learning is a subset where a large cluster of machines is used to analyze and extract knowledge from large datasets.

Machine learning is closely related to computational statistics. It has strong ties to mathematical optimization; it provides methods, theory, and application domains to the field. Machine learning is employed in various types of computing tasks where designing and programming explicit algorithms are infeasible. Example applications are spam filtering, optical character recognition (OCR), search engine, and computer vision. Machine learning is sometimes combined with data mining, which focuses more on exploratory data analysis and is known as unsupervised learning.

Machine learning systems can be classified into three categories, depending on the nature of the learning signal available to a learning system. Learning algorithm discovers structure from the input provided. It can have a goal (hidden patterns), or it could be a means try to find features.

• Unsupervised learning: No labels of outputs are given to the learning system. It finds structure on its own from the inputs given to
• Supervised learning: The system is presented with inputs and desired outputs by a human and the goal is to learn a model to map inputs to outputs
• Reinforcement learning: The system interacts with an environment in which it performs a stated goal without a human explicitly telling it whether it has come close to its goal

In the later sections, we will map supervised and unsupervised learning to various chapters.